Why telecom AI programs stall before they reach the model
Every operator already has AI programs underway, but most networks were never built to feed them. To keep data affordable, networks sample it, keep it for days instead of years and store it separately by domain. That leaves AI working from incomplete data. This paper explains why those compromises are no longer necessary, and how OcientAIQ™ works alongside the cloud, lakehouse and governance platforms operators already run.
Inside the paper
- The six compromises: What every network gave up to keep its data affordable, and what that costs once AI is involved.
- Many domains, one question: Why root cause requires joining user plane, control plane, telemetry, OSS and BSS data in a single query.
- A carrier-grade data foundation: Five requirements, from ingest at line rate to years of full-resolution retention.
- What agents change: Why accurate, accountable agents depend on deterministic computation at the data layer.
